Predict Bank Customer Churning

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as ex
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import plotly.offline as pyo
pyo.init_notebook_mode()
sns.set_style('darkgrid')
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import f1_score as f1
from sklearn.metrics import confusion_matrix
import scikitplot as skplt

plt.rc('figure',figsize=(18,9))

from imblearn.over_sampling import SMOTE

data = pd.read_csv("BankChurners.csv")

data.head()

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	...	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio	Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1	Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	...	12691.0	777	11914.0	1.335	1144	42	1.625	0.061	0.000093	0.99991
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	...	8256.0	864	7392.0	1.541	1291	33	3.714	0.105	0.000057	0.99994
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	...	3418.0	0	3418.0	2.594	1887	20	2.333	0.000	0.000021	0.99998
3	769911858	Existing Customer	40	F	4	High School	Unknown	Less than $40K	Blue	34	...	3313.0	2517	796.0	1.405	1171	20	2.333	0.760	0.000134	0.99987
4	709106358	Existing Customer	40	M	3	Uneducated	Married	$60K - $80K	Blue	21	...	4716.0	0	4716.0	2.175	816	28	2.500	0.000	0.000022	0.99998

5 rows × 23 columns

Exploratory Data Analysis

fig = make_subplots(rows=2, cols=1)
r1 = go.Box(x = data["Customer_Age"],name = "Age Box Plot",boxmean = True)
r2 = go.Histogram(x = data["Customer_Age"],name = "Age Histogram")

fig.add_trace(r1,row=1,col = 1)
fig.add_trace(r2,row=2,col = 1)

fig.update_layout(height = 700,width = 1200,title_text = "Distribution of Customer Ages")
fig.show()

## Our customer ages feature follows normal distribution

fig = make_subplots(rows = 2 , cols = 2 , subplot_titles = ("",
                                                         '<b>Platinum Card Holders<b>',
                                                         "<b>Blue Card Holders<b>",
                                                         "Residuals"),
                  vertical_spacing = 0.09,
                  specs = [[{"type":"pie","rowspan":2},
                           {"type" : "pie"}],
                          [None ,{"type" : "pie"}]])

fig.add_trace(go.Pie(labels = ["Female Platinum Card Holders",
                              "Male Platinum Card Holders"],
                    values = data.query('Card_Category == "Platinum"').Gender.value_counts().values,
                    pull = [0,0.05,0.5],
                    hole = 0.3),
             row = 1,
             col = 2)

fig.add_trace(go.Pie(labels = ["Female Platinum Card Holders",
                              "Male Platinum Card Holders"],
                    values = data.query('Card_Category == "Blue"').Gender.value_counts().values,
                    pull = [0,0.2,0.5],
                    hole = 0.3),
             row = 2,
             col = 2)

More samples of female in the dataset compared to males

fig = make_subplots(rows=2, cols=1)
r1 = go.Box(x = data["Dependent_count"],name = "Dependent count Box Plot",boxmean = True)
r2 = go.Histogram(x = data["Dependent_count"],name = "Dependent count Histogram")

fig.add_trace(r1,row=1,col = 1)
fig.add_trace(r2,row=2,col = 1)

fig.update_layout(height = 700,width = 1200,title_text = "Distribution of Dependent counts")
fig.show()

The distribution of Dependent counts is fairly normally

ex.pie(data,names = "Education_Level",title = "Propotion Of Education Levels",hole = 0.3)

We have about 30 percentage of customer unknown eduaction

ex.pie(data,names = "Marital_Status",title = "Propotion of different Marriage Status",hole = 0.3)

About half of the customers are married and about 7 % customers are divorced

ex.pie(data,names = "Card_Category",title = "Propotion of different Card Category",hole = 0.3)

ex.pie(data,names = "Income_Category",title = "Propotion of different Income Category",hole = 0.3)

fig = make_subplots(rows=2, cols=1)
r1 = go.Box(x = data["Months_on_book"],name = "Months on book Box Plot",boxmean = True)
r2 = go.Histogram(x = data["Months_on_book"],name = "Months on book Histogram")

fig.add_trace(r1,row=1,col = 1)
fig.add_trace(r2,row=2,col = 1)

fig.update_layout(height = 700,width = 1200,title_text = "Distribution of Dependent counts")
fig.show()

print('Kurtosis of Months on book features is : {}'.format(data['Months_on_book'].kurt()))

Kurtosis of Months on book features is : 0.40010012019986707

We have a low kurtosis value pointing to a very flat shaped distribution (as shown in the plots above as well), meaning we cannot assume normality of the feature.

fig = make_subplots(rows=2, cols=1)
r1 = go.Box(x = data["Total_Relationship_Count"],name = "Total Relationship Count Box Plot",boxmean = True)
r2 = go.Histogram(x = data["Total_Relationship_Count"],name = "Total Relationship Count Histogram")

fig.add_trace(r1,row=1,col = 1)
fig.add_trace(r2,row=2,col = 1)

fig.update_layout(height = 700,width = 1200,title_text = "Distribution of Total Relationship")
fig.show()

The distribution of the total number of products held by the customer seems closer to a uniform distribution and may appear useless as a predictor for churn status.

fig = make_subplots(rows=2, cols=1)
r1 = go.Box(x = data["Months_Inactive_12_mon"],name = "Num Of Months Inactive Box Plot",boxmean = True)
r2 = go.Histogram(x = data["Months_Inactive_12_mon"],name = "Num Of Months Inactive Histogram")

fig.add_trace(r1,row=1,col = 1)
fig.add_trace(r2,row=2,col = 1)

fig.update_layout(height = 700,width = 1200,title_text = "Distribution of Num Of Months Inactive")
fig.show()

fig = make_subplots(rows=2, cols=1)
r1 = go.Box(x = data["Credit_Limit"],name = "Credit Limit Box Plot",boxmean = True)
r2 = go.Histogram(x = data["Credit_Limit"],name = "Credit Limit Histogram")

fig.add_trace(r1,row=1,col = 1)
fig.add_trace(r2,row=2,col = 1)

fig.update_layout(height = 700,width = 1200,title_text = "Distribution of Credit_Limit")
fig.show()

fig = make_subplots(rows=2, cols=1)
r1 = go.Box(x = data["Total_Trans_Amt"],name = "Total_Trans_Amt Box Plot",boxmean = True)
r2 = go.Histogram(x = data["Total_Trans_Amt"],name = "Total_Trans_Amt Histogram")

fig.add_trace(r1,row=1,col = 1)
fig.add_trace(r2,row=2,col = 1)

fig.update_layout(height = 700,width = 1200,title_text = "Distribution of Total_Trans_Amt")
fig.show()

As we can see the plot sepaated to four plots so we ca divide this feature to groups and see the results

ex.pie(data,names='Attrition_Flag',title='Proportion of churn vs not churn customers',hole=0.33)

So here we have ony 16% data samples represent churn customers , so we need to upsample the churn samples to match them with regular customer sample size to give the later selected models the chance to return a better results

data.shape

(10127, 23)

data = data[data.columns[:-2]]

data.shape

(10127, 21)

data.head(3)

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	...	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	...	1	3	12691.0	777	11914.0	1.335	1144	42	1.625	0.061
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	...	1	2	8256.0	864	7392.0	1.541	1291	33	3.714	0.105
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	...	1	0	3418.0	0	3418.0	2.594	1887	20	2.333	0.000

3 rows × 21 columns

Data preprocessing

#### Encode feature vales 

data.Attrition_Flag.unique()

array(['Existing Customer', 'Attrited Customer'], dtype=object)

data.Attrition_Flag = data.Attrition_Flag.replace({'Existing Customer' : 0, 'Attrited Customer' : 1})

data.Gender.unique()

array(['M', 'F'], dtype=object)

data.Gender = data.Gender.replace({'M' : 0, 'F' : 1})

data = pd.concat([data,pd.get_dummies(data["Education_Level"]).drop(columns = ["Unknown"])],axis = 1)

data = pd.concat([data,pd.get_dummies(data["Income_Category"]).drop(columns = ["Unknown"])],axis = 1)

data = pd.concat([data,pd.get_dummies(data["Marital_Status"]).drop(columns = ["Unknown"])],axis = 1)

data = pd.concat([data,pd.get_dummies(data["Card_Category"])],axis = 1)

data.drop(columns = ["Education_Level",
                    "Income_Category",
                    "Marital_Status",
                    "Card_Category",
                    "CLIENTNUM"],inplace = True)

data.shape

(10127, 34)

Now we did one hot encode for all the categorical features describing different statuses of a customer.

fig = make_subplots(rows=2, cols=1,shared_xaxes=True,subplot_titles=('Perason Correaltion',  'Spearman Correaltion'))
colorscale=     [[1.0              , "rgb(165,0,38)"],
                [0.8888888888888888, "rgb(215,48,39)"],
                [0.7777777777777778, "rgb(244,109,67)"],
                [0.6666666666666666, "rgb(253,174,97)"],
                [0.5555555555555556, "rgb(254,224,144)"],
                [0.4444444444444444, "rgb(224,243,248)"],
                [0.3333333333333333, "rgb(171,217,233)"],
                [0.2222222222222222, "rgb(116,173,209)"],
                [0.1111111111111111, "rgb(69,117,180)"],
                [0.0               , "rgb(49,54,149)"]]

s_val =data.corr('pearson')
s_idx = s_val.index
s_col = s_val.columns
s_val = s_val.values
fig.add_trace(
    go.Heatmap(x=s_col,y=s_idx,z=s_val,name='pearson',showscale=False,xgap=0.7,ygap=0.7,colorscale=colorscale),
    row=1, col=1
)

s_val =data.corr('spearman')
s_idx = s_val.index
s_col = s_val.columns
s_val = s_val.values
fig.add_trace(
    go.Heatmap(x=s_col,y=s_idx,z=s_val,xgap=0.7,ygap=0.7,colorscale=colorscale),
    row=2, col=1
)
fig.update_layout(
    hoverlabel=dict(
        bgcolor="white",
        font_size=16,
        font_family="Rockwell"
    )
)
fig.update_layout(height=700, width=900, title_text="Numeric Correaltions")
fig.show()

Data Upsampling

data.head(2)

	Attrition_Flag	Customer_Age	Gender	Dependent_count	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	...	$60K - $80K	$80K - $120K	Less than $40K	Divorced	Married	Single	Blue	Gold	Platinum	Silver
0	0	45	0	3	39	5	1	3	12691.0	777	...	1	0	0	0	1	0	1	0	0	0
1	0	49	1	5	44	6	1	2	8256.0	864	...	0	0	1	0	0	1	1	0	0	0

2 rows × 34 columns

data.shape

(10127, 34)

oversampler = SMOTE()

X,y = oversampler.fit_resample(data[data.columns[1:]],data[data.columns[0]])

usampled_df = X.assign(Churn = y)

ohe_data =usampled_df[usampled_df.columns[15:-1]].copy()

usampled_df = usampled_df.drop(columns=usampled_df.columns[15:-1])

fig = make_subplots(rows=2, cols=1,shared_xaxes=True,subplot_titles=('Perason Correaltion',  'Spearman Correaltion'))
colorscale=     [[1.0              , "rgb(165,0,38)"],
                [0.8888888888888888, "rgb(215,48,39)"],
                [0.7777777777777778, "rgb(244,109,67)"],
                [0.6666666666666666, "rgb(253,174,97)"],
                [0.5555555555555556, "rgb(254,224,144)"],
                [0.4444444444444444, "rgb(224,243,248)"],
                [0.3333333333333333, "rgb(171,217,233)"],
                [0.2222222222222222, "rgb(116,173,209)"],
                [0.1111111111111111, "rgb(69,117,180)"],
                [0.0               , "rgb(49,54,149)"]]

s_val =usampled_df.corr('pearson')
s_idx = s_val.index
s_col = s_val.columns
s_val = s_val.values
fig.add_trace(
    go.Heatmap(x=s_col,y=s_idx,z=s_val,name='pearson',showscale=False,xgap=0.7,ygap=0.7,colorscale=colorscale),
    row=1, col=1
)

s_val =usampled_df.corr('spearman')
s_idx = s_val.index
s_col = s_val.columns
s_val = s_val.values
fig.add_trace(
    go.Heatmap(x=s_col,y=s_idx,z=s_val,xgap=0.7,ygap=0.7,colorscale=colorscale),
    row=2, col=1
)
fig.update_layout(
    hoverlabel=dict(
        bgcolor="white",
        font_size=16,
        font_family="Rockwell"
    )
)
fig.update_layout(height=700, width=900, title_text="Numeric Correaltions")
fig.show()

Dimensional Reduction

We will use principal component analysis to reduce the dimensionality of the one-hot encoded categorical variables losing some of the variances, but simultaneously, using a couple of principal components instead of tens of one-hot encoded features will help me construct a better model.

n_components = 4

pca = PCA(n_components = n_components)

pca_matrix = pca.fit_transform(ohe_data)

var_ratio = pca.explained_variance_ratio_

total_variance = var_ratio.sum() * 100

cumsum_var = np.cumsum(var_ratio)

trace_1 = {
    "name" : "individual explained variance",
    "type" : "bar",
    "y" : var_ratio
}
trace_2 = {
    "name" : "cumulative explained variance",
    "type" : "scatter",
    "y" : cumsum_var
}
all_trace = [trace_1 , trace_2]

layout = {
    "xaxis" : {"title" : "Principal components"},
    "yaxis" : {"title" : "Explained variance ratio"}
}

fig = go.Figure(data = all_trace,layout = layout)
fig.update_layout(title='Explained Variance Using {} Dimensions'.format(n_components))
fig.show()

pca_matrix

array([[-0.6932013 , -0.38522413, -0.3477231 ,  0.70411828],
       [ 0.71628134,  0.91159335,  0.65810905,  0.15016223],
       [-0.72216953, -0.17895787,  0.86476066,  0.14095942],
       ...,
       [ 0.06455727, -0.27962424, -0.18342383, -0.47177497],
       [ 0.73494675, -0.22045558, -0.06673375,  0.08430329],
       [ 0.07471312,  0.64172398, -0.36076762, -0.44334439]])

usampled_df_with_pcs = pd.concat([usampled_df,pd.DataFrame(pc_matrix,columns=['PC-{}'.format(i) for i in range(0,n_components)])],axis=1)

usampled_df_with_pcs.head()

	Customer_Age	Gender	Dependent_count	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio	Churn	PC-0	PC-1	PC-2	PC-3
0	45	0	3	39	5	1	3	12691.0	777	11914.0	1.335	1144	42	1.625	0.061	0	-0.691288	-0.380578	-0.342055	0.724169
1	49	1	5	44	6	1	2	8256.0	864	7392.0	1.541	1291	33	3.714	0.105	0	0.708180	0.895062	0.691294	0.139054
2	51	0	3	36	4	1	0	3418.0	0	3418.0	2.594	1887	20	2.333	0.000	0	-0.720019	-0.214818	0.864182	0.116777
3	40	1	4	34	3	4	1	3313.0	2517	796.0	1.405	1171	20	2.333	0.760	0	0.059752	0.618470	-0.638240	0.156062
4	40	0	3	21	5	1	0	4716.0	0	4716.0	2.175	816	28	2.500	0.000	0	-0.679911	-0.348096	-0.180897	0.145732

fig = ex.scatter_matrix(
usampled_df_with_pcs[["PC-{}".format(i) for i in range(0,n_components)]].values,
color = usampled_df_with_pcs.Credit_Limit,
dimensions = range(n_components),
labels = {str(i):"PC-{}".format(i) for i in range(0,n_components)},
title = f'Total Explained Variance :{total_variance:.2f}%')

fig.update_traces(diagonal_visible = False)
fig.update_layout(coloraxis_colorbar = dict(title = "Credit_Limit"))
fig.show()

fig = make_subplots(rows=2,cols = 1,shared_xaxes = True , subplot_titles = ("Perason Correaltion" , "Spearman Correaltion"))

s_val = usampled_df_with_pcs.corr("pearson")
s_idx = s_val.index
s_col = s_val.columns
s_val = s_val.values

fig.add_trace(go.Heatmap(x = s_col,y = s_idx,z = s_val,
             name = "pearson",showscale = False,
             xgap = 1,ygap = 1,
             colorscale = colorscale),row = 1,col = 1)

fig.add_trace(go.Heatmap(x = s_col,y = s_idx,z = s_val,
             name = "spearman",showscale = False,
             xgap = 1,ygap = 1,
             colorscale = colorscale),row = 2,col = 1)

fig.update_layout(
hoverlabel = dict(
bgcolor = "white",
font_size = 16,
font_family = "Rockwell"))

fig.update_layout(height = 700,width = 900,
                 title_text = "Upsampled Correlations With PC\'s'")
fig.show()

Model Selection and Evaluation

X_features = ['Total_Trans_Ct','PC-3','PC-1','PC-0','PC-2','Total_Ct_Chng_Q4_Q1','Total_Relationship_Count']

X = usampled_df_with_pcs[X_features]
y = usampled_df_with_pcs['Churn']

X.shape

(17000, 7)

y.shape

(17000,)

x_train,x_test,y_train,y_test = train_test_split(X,y,random_state = 42)

Cross Validation

rf = Pipeline(steps=[("scale",StandardScaler()),
                    ("RF",RandomForestClassifier(random_state=42))])

ada = Pipeline(steps=[("scale",StandardScaler()),
                    ("ADA",AdaBoostClassifier(random_state=42,learning_rate=0.5))])

svm = Pipeline(steps=[("scale",StandardScaler()),
                    ("SVM",SVC(random_state=42,kernel='rbf'))])

f1_cross_val_scores = cross_val_score(rf,x_train,y_train,cv=5,scoring='f1')

ada_cross_val_scores = cross_val_score(ada,x_train,y_train,cv=5,scoring='f1')

svm_cross_val_scores = cross_val_score(svm,x_train,y_train,cv=5,scoring='f1')

fig = make_subplots(rows = 3,cols = 1,shared_xaxes = True,subplot_titles = (
"random_forest",
"Adaboost",
"SVM"))

fig.add_trace(
go.Scatter(x = list(range(0,len(f1_cross_val_scores))),y = f1_cross_val_scores,name='Random Forest'),row=1, col=1)
fig.add_trace(
go.Scatter(x = list(range(0,len(ada_cross_val_scores))),y = ada_cross_val_scores,name='Adaboost'),row=2, col=1)
fig.add_trace(
go.Scatter(x = list(range(0,len(svm_cross_val_scores))),y = svm_cross_val_scores,name='SVM'),row=3, col=1)


fig.update_layout(height = 700,width = 900, title = "Different Model 5 Fold Cross Validation")
fig.update_yaxes(title_text = "F1 Score")
fig.update_xaxes(title_text = "Fold #")

fig.show()

Training

rf.fit(x_train,y_train)
rf_pred = rf.predict(x_test)

ada.fit(x_train,y_train)
ada_pred = ada.predict(x_test)

svm.fit(x_train,y_train)
svm_pred = svm.predict(x_test)

fig = go.Figure(data = [go.Table(header = dict(values = ['<b>Model<b>',
                                                        '<b>F1 Score on Test Data<b>'],
                                              line_color="darkslategray",
                                              fill_color = "whitesmoke",
                                              align = ['center','center'],
                                              font = dict(color = "black",size = 18),
                                              height = 40),
                                
                                cells = dict(values = [['<b>Random Forest<b>',
                                                       '<b>Adaboost<b>',
                                                       "<b>SVM<b>"],
                                                      [np.round(f1(rf_pred,y_test),2),
                                                      np.round(f1(ada_pred,y_test),2),
                                                      np.round(f1(svm_pred,y_test),2)]]))
                       ])
fig.update_layout(title='Model Results On Test Data')
fig.show()

Model Evaluation On Original Data (Before Upsampling)

orig_data = data[data.columns[16:]].copy()

orig_data.head()

	College	Doctorate	Graduate	High School	Post-Graduate	Uneducated	$120K +	$40K - $60K	$60K - $80K	$80K - $120K	Less than $40K	Divorced	Married	Single	Blue	Gold	Platinum	Silver
0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	1	0	0	0
1	0	0	1	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0
2	0	0	1	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0
3	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0
4	0	0	0	0	0	1	0	0	1	0	0	0	1	0	1	0	0	0

pca_matrix = pca.fit_transform(orig_data)
orig_data_with_pcs = pd.concat([data,pd.DataFrame(pca_matrix,columns=["PC-{}".format(i) for i in range(0,n_components)])],axis = 1)

unsampled_data_rf = rf.predict(orig_data_with_pcs[X_features])

unsampled_data_ada = ada.predict(orig_data_with_pcs[X_features])

unsampled_data_svm = svm.predict(orig_data_with_pcs[X_features])

fig = go.Figure(data = [go.Table(header = dict(values = ['<b>Model<b>',
                                                        '<b>F1 Score on Test Data Without Upsampling<b>'],
                                              line_color="darkslategray",
                                              fill_color = "whitesmoke",
                                              align = ['center','center'],
                                              font = dict(color = "black",size = 18),
                                              height = 40),
                                
                                cells = dict(values = [['<b>Random Forest<b>',
                                                       '<b>Adaboost<b>',
                                                       "<b>SVM<b>"],
                                                      [np.round(f1(unsampled_data_rf,orig_data_with_pcs.Attrition_Flag),2),
                                                      np.round(f1(unsampled_data_ada,orig_data_with_pcs.Attrition_Flag),2),
                                                      np.round(f1(unsampled_data_svm,orig_data_with_pcs.Attrition_Flag),2)]]))
                       ])
fig.update_layout(title='Model Results On Test Data Without UPSAMPLING')
fig.show()

Conclusion

conf_mat = confusion_matrix(unsampled_data_rf,orig_data_with_pcs.Attrition_Flag)

fig = ff.create_annotated_heatmap(conf_mat,
                                 x = ["Not Churn","Churn"],
                                 y = ["Predicted Not Churn","Predicted Churn"],
                                 colorscale = "Fall" , xgap = 3,ygap=3)
fig["data"][0]['showscale'] = True
fig.update_layout(title = "Prediction On Original Data using Random Forest")

fig.show()

unsampled_data_rf = rf.predict_proba(orig_data_with_pcs[X_features])
skplt.metrics.plot_precision_recall(orig_data_with_pcs['Attrition_Flag'], unsampled_data_rf)
plt.legend(prop={'size': 20})

<matplotlib.legend.Legend at 0x25923802b38>